A key challenge for efficient learning systems is to learn new information without forgetting information gained previously.
Solving this challenge is crucial for deep neural networks (DNNs), which seek to continue learning new tasks without catastrophically forgetting old ones [1]}, [2]}, [3]}, .
Because the participating tasks are varied and may have conflicting objectives, we can view task-incremental learning as a complex, multi-objective optimization problem [4]}.
Broadly, two main approaches have been proposed to address this problem.
The first set of methods impose some form of regularization on a fixed capacity network, by restricting changes to important parameters of each task [5]}, [6]}, [7]} or using constrained experience replay [8]}, [9]}.
The first set of methods impose some form of regularization on a fixed capacity network, by restricting changes to important parameters of each task [5]}, [6]}, [7]}, using constrained experience replay [8]}, [9]} or distillation [15]}.
The second set of methods tackle forgetting by dynamic network expansion, i.e., adding additional task-specific parameters when a new task arrives.
The challenge here is to minimize the expansion and avoid exploding the network parameters during learning.
In other words, the expansion based approaches deal with the expand-vs-forget dilemma, a variant of the classical stability-plasticity dilemma [16]}.
